Identification of cancer protein biomarkers using proteomic techniques

ABSTRACT

The claimed invention describes methods to diagnose or aid in the diagnosis of cancer. The claimed methods are based on the identification of biomarkers which are particularly well suited to discriminate between cancer subjects and healthy subjects. These biomarkers were identified using a unique and novel screening method described herein. The biomarkers identified herein can also be used in the prognosis and monitoring of cancer. The invention comprises the use of leptin, prolactin, OPN and IGF-II for diagnosing, prognosis and monitoring of ovarian cancer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/602,916, filed Jan. 22, 2015, which is a continuation of U.S.application Ser. No. 12/644,677, filed Dec. 22, 2009, now U.S. Pat. No.8,975,379, which is a continuation of U.S. application Ser. No.11/037,889, filed Jan. 18, 2005, now U.S. Pat. No. 7,666,583, whichclaims the benefit of U.S. Provisional Application No. 60/545,581, filedFeb. 19, 2004, and U.S. Provisional Application No. 60/545,900, filedFeb. 20, 2004. The teachings of each referenced application areincorporated by reference herein.

FUNDING

This invention was made with government support under grant numberDE-FG02-ER63462 awarded by the United States Department of Energy. Thegovernment has certain rights in the invention.

BACKGROUND OF THE INVENTION

Epithelial Ovarian Cancer (EOC) is the fourth leading cause ofcancer-related death in women in the United States and the leading causeof gynecologic cancer death. EOC is characterized by few early symptoms,presentation at an advanced stage, and poor survival. This yearapproximately 25,000 women will be newly diagnosed with ovarian cancerand 13,500 will die from the disease. The major limitations in thetreatment of ovarian cancer are: i) the lack of an early detection tumormarker, ii) the resistance to chemotherapeutic agents, and iii) the lackof obvious early warning symptoms. The high mortality rate is related tothe inability to detect early disease, as approximately 70% of patientsare diagnosed at an advanced stage. In patients diagnosed with early(stage I or II) disease, the five-year survival rate ranges from 60 to90% depending on the degree of tumor differentiation. Although theclinical presentation of heritable cancer is similar to the high-riskpopulation, the onset of ovarian cancer in this group tends to occur10-15 years earlier than that of the general population (early 40'srather than 60's). One of the most promising approaches to management ofEOC is early detection. The most commonly used test, CA125 identifies agroup of cell surface glycoproteins that have uncertain biologicalbehavior and very limited clinical application for the detection ofearly stage disease. As a single marker, CA125 has a predictive value ofless than 10% in Stage I. Even the addition of ultrasound screening toCA125 measurement improves the positive prediction value to only about20%. The lack of specific markers for ovarian cancer makes it difficultto achieve the clinical objective of screening and early detection.

Presently there is no commercially available test that can be used todiagnose either early or advanced stage ovarian cancer. Thus, theidentification of a test that can be used to diagnose early or advancestage ovarian cancer is required.

BRIEF SUMMARY OF THE INVENTION

The invention comprises a method for diagnosing or aiding in thediagnosis of cancer in a subject comprising comparing the expression ofone or more biomarkers in a sample from a subject to a predeterminedstandard for each said one or more biomarkers; wherein said one or morebiomarkers are selected from the group consisting of: 6Ckine, ACE, BDNF,CA125, E-Selectin, EGF, Eot2, ErbB1, follistatin, HCC4, HVEM, IGF-II,IGFBP-1, IL-17, IL-1srII, IL-2sRa, leptin, M-CSF R, MIF, MIP-la, MIP3b,MMP-8, MMP7, MPIF-1, OPN, PARC, PDGF Rb, prolactin, ProteinC, TGF-bRIII, TNF-R1, TNF-a, VAP-1, VEGF R2 and VEGF R3; and wherein asignificant difference in the expression of said one or more biomarkersin said sample as compared to a predetermined standard of each said oneor more biomarkers diagnoses or aids in the diagnosis of cancer.

In one embodiment, the predetermined standard corresponds to: (a) theexpression level of said biomarker in healthy subjects, or (b) theexpression level of said biomarker in non-cancerous tissue from the samesubject.

In one embodiment, the method further comprises comparing the expressionof two or more biomarkers, wherein the diagnosis of cancer is based on ascore-based classification method. In one embodiment, the methodcomprises comparing the expression of m different biomarkers; whereineach biomarker is assigned a score of 0 or 1, wherein a biomarker isassigned a score of 0 if the expression of said biomarker is notsignificantly different from the expression of said biomarker in apredetermined standard and wherein a biomarker is assigned a score of 1if the expression of said biomarker is significantly different from theexpression of said biomarker in a predetermined standard; wherein thesubject is assigned an overall score which corresponds to the sum of theassigned scores from m different markers; and wherein a given threshold(t) is used to diagnose or aid in the diagnosis of cancer.

In another embodiment, the method comprises comparing the expression oftwo or more biomarkers, wherein the diagnosis of cancer is made bycomparing the expression profile of said two or more biomarkers to apredetermined standard profile for said biomarkers, and wherein adifference in the profiles diagnoses or aids in the diagnosis of cancer.In one embodiment, the predetermined standard profile is determined bycomparing the expression of said two or more biomarkers in cancersubjects to the expression of said two or more biomarkers in healthysubjects using a machine learning technique. In one embodiment, thepredetermined standard profile is determined by comparing the expressionof said two or more biomarkers in cancer subjects and in healthysubjects using support vector machines, K-nearest neighbor classifier,or classification tree analysis.

In one embodiment, the method is for the diagnosis for ovarian cancer,and the method further comprises detecting an additional biomarker forovarian cancer which is not identified in Table 2. In one embodiment,the additional biomarker for ovarian cancer may be selected from thegroup consisting of: human stratum corneum chymotryptic enzyme (HSCCE),kallikrein 4, kallikrein 5, kallikrein 6 (protease M), kallikrein 8,kallikrein 9, kallikrein 10, CA125, CA15-3, CA19-9, OVX1,lysophosphatidic acid (LPA), carcinoebryonic antigen (CEA), macrophagecolony-stimulating factor (M-CSF), prostasin, CA54-61, CA72, HMFG2,IL-6, IL-10, LSA, M-CSF, NB70K, PLAP, TAG72, TNF, TPA, UGTF, WAPfour-disulfide core domain 2 (HE4), matrix metalloprotease 2,tetranectin, inhibin, mesothelyn, MUC1, VEGF, CLDN3, NOTCH3, E2Ftranscription factor 3 (E2F3), GTPase activating protein (RACGAP1),hemotological and neurological expressed 1 (HN1), apolipoprotein A1,laminin, claudin 3, claudin 4, tumor-associated calcium signaltransducer 1 (TROP-1/Ep-CAM), tumor-associated calcium signal transducer2 (TROP-2), ladinin 1, S100A2, SERPIN2 (PAI-2), CD24, lipocalin 2,matriptase (TADG-15), stratifin, transforming growth factor-betareceptor III, platelet-derived growth factor receptor alpha, SEMACAP3,ras homology gene family member I (ARHI), thrombospondin 2,disabled-2/differentially expressed in ovarian carcinoma 2 (Dab2/DOC2),and haptoglobin-alpha subunit. In another embodiment, the additionalbiomarker for ovarian cancer is the truncated form of transthyretin orthe cleavage fragment of inter-alpha-trypsin inhibitor heavy chain H4identified by Zhang et al., Cancer Res. 64(16):5882-90 (2004). In oneembodiment, the additional biomarker for ovarian cancer is CA125.

The above described methods of diagnosing or aiding in the diagnosis ofcancer can be applied to diagnose or aid in the diagnosis of any canceror tumor. In one embodiment, the method is for the diagnosis of breastcancer. In one embodiment, the method is for the diagnosis of coloncancer. In another embodiment, the method is for the diagnosis ofcervical cancer.

The invention also comprises a method for diagnosing or aiding in thediagnosis of cancer in a subject comprising comparing the expression ofone or more biomarkers in a sample from a subject to a predeterminedstandard for each said one or more biomarkers; wherein said one or morebiomarkers are selected from the group consisting of: prolactin, MIF,OPN, IGF-II, E-Selectin, leptin, EGF, IL-17, MPIF-1, and IL-2sRa; andwherein a significant difference in the expression of said one or morebiomarkers in said sample as compared to a predetermined standard ofeach said one or more biomarkers diagnoses or aids in the diagnosis ofcancer.

The invention also comprises a method for diagnosing or aiding in thediagnosis of cancer in a subject comprising comparing the expression ofone or more biomarkers in a sample from a subject to a predeterminedstandard for each said one or more biomarkers; wherein said one or morebiomarkers are selected from the group consisting of: leptin, prolactin,OPN and IGF-II; and wherein a significant difference in the expressionof one or more biomarkers in said sample as compared to a predeterminedstandard of each said one or more biomarkers diagnoses or aids in thediagnosis of cancer.

The invention also comprises a method for diagnosing or aiding in thediagnosis of cancer in a subject comprising comparing the expression ofthe following four biomarkers: leptin, prolactin, OPN and IGF-II, in asample from a subject to a predetermined standard for each saidbiomarkers; wherein a significant difference in the expression of two ormore of said biomarkers in said sample as compared to a predeterminedstandard of each said one or more biomarkers diagnoses or aids in thediagnosis of cancer.

The invention also comprises a method for diagnosing or aiding in thediagnosis of ovarian cancer in a subject comprising comparing theexpression of the following four biomarkers: leptin, prolactin, OPN andIGF-II, in a sample from a subject to a predetermined standard for eachsaid biomarkers; wherein a significant difference in the expression oftwo or more of said biomarkers in said sample as compared to apredetermined standard of each said one or more biomarkers diagnoses oraids in the diagnosis of cancer.

The invention also comprises a method for diagnosing or aiding in thediagnosis of breast cancer in a subject comprising comparing theexpression of the following four biomarkers: leptin, prolactin, OPN andIGF-II, in a sample from a subject to a predetermined standard for eachsaid biomarkers; wherein a significant difference in the expression oftwo or more of said biomarkers in said sample as compared to apredetermined standard of each said one or more biomarkers diagnoses oraids in the diagnosis of cancer.

The invention also comprises a method for diagnosing or aiding in thediagnosis of colon cancer in a subject comprising comparing theexpression of the following four biomarkers: leptin, prolactin, OPN andIGF-II, in a sample from a subject to a predetermined standard for eachsaid biomarkers; wherein a significant difference in the expression oftwo or more of said biomarkers in said sample as compared to apredetermined standard of each said one or more biomarkers diagnoses oraids in the diagnosis of cancer.

In one embodiment, the above described methods comprise comparing theexpression of prolactin and/or OPN to a predetermined standard of saidbiomarker, wherein an increase in the expression of said biomarker ascompared to the predetermined standard for said biomarker diagnoses oraids in the diagnosis of cancer.

In one embodiment, the above described methods comprise comparing theexpression of leptin and/or IGF-II to a predetermined standard of saidbiomarker, and wherein a decrease in the expression of said biomarker ascompared to the predetermined standard for said biomarker diagnoses oraids in the diagnosis of cancer.

In one embodiment, the above described methods of diagnosing or aidingin the diagnosis of cancer comprises detecting the expression of two ormore biomarkers. In one embodiment, said two or more biomarkers areselected from the group consisting of: prolactin, MIF, OPN, IGF-II,E-Selectin, leptin, EGF, IL-17, MPIF-1, and IL-2sRa. In one embodiment,said two or more biomarkers are selected from the group consisting of:leptin, prolactin, OPN and IGF-II. In one embodiment, a significantdifference in the expression of at least two or said two or morebiomarkers diagnoses or aids in the diagnosis of cancer.

In one embodiment, the above described methods of diagnosing or aidingin the diagnosis of cancer comprises comparing the expression of threeor more biomarkers. In one embodiment, said three or more biomarkers areselected from the group consisting of: leptin, prolactin, OPN andIGF-II. In one embodiment, a significant difference in the expression ofsaid three or more biomarkers diagnoses or aids in the diagnosis ofcancer.

In one embodiment, the above described methods of diagnosing or aidingin the diagnosis of cancer comprises comparing the expression of four ormore biomarkers. In one embodiment, said four or more biomarkers includeleptin, prolactin, OPN and IGF-II. In one embodiment, a significantdifference in the expression of four or more biomarkers diagnoses oraids in the diagnosis of cancer.

In one embodiment, the expression of a biomarker is detected or measuredusing a reagent that detects said one or more biomarkers. In oneembodiment, the reagent is an antibody or fragment thereof specific forsaid one or more biomarkers. In one embodiment, the reagent is directlyor indirectly labeled with a detectable substance. In anotherembodiment, the expression of said one or more biomarker is detectedusing mass spectroscopy. In another embodiment, the expression of saidone or more biomarker is detected by measuring the mRNA transcriptionlevels of the gene encoding said one or more biomarker.

In another embodiment, the expression of said one or more biomarker isdetected by: (a) detecting the expression of a polypeptide which isregulated by said one or more biomarker; (b) detecting the expression ofa polypeptide which regulates said biomarker; or (c) detecting theexpression of a metabolite of said biomarker.

In one embodiment, the sample used in the above described methods is abody fluid sample. In one embodiment, the body fluid sample is blood orserum.

The invention also comprises methods for monitoring the progression ofcancer in a subject. In one embodiment, the invention comprises a methodof monitoring the progression of cancer in a subject comprisingcomparing the expression of one or more biomarkers in a sample from asubject to the expression of said one or more biomarkers in a sampleobtained from the subject at a subsequent point in time; wherein saidone or more biomarkers are selected from the group consisting of:6Ckine, ACE, BDNF, CA125, E-Selectin, EGF, Eot2, ErbB1, follistatin,HCC4, HVEM, IGF-II, IGFBP-1, IL-17, IL-1srII, IL-2sRa, leptin, M-CSF R,MIF, MIP-1a, MIP3b, MMP-8, MMP7, MPIF-1, OPN, PARC, PDGF Rb, prolactin,ProteinC, TGF-b RIII, TNF-R1, TNF-a, VAP-1, VEGF R2 and VEGF R3; andwherein a difference in the expression of said one or more biomarkerdiagnoses or aids in the diagnosis of the progression of the cancer inthe subject. In one embodiment, said one or more biomarkers are selectedfrom the group consisting of: prolactin, MIF, OPN, IGF-II, E-Selectin,leptin, EGF, IL-17, MPIF-1, and IL-2sRa. In one embodiment, said one ormore biomarkers are selected from the group consisting of: leptin,prolactin, OPN and IGF-II.

In one embodiment, the above described methods of monitoring theprogression of cancer comprises comparing the expression of two or morebiomarkers. In one embodiment, said two or more biomarkers are selectedfrom the group consisting of: prolactin, MIF, OPN, IGF-II, E-Selectin,leptin, EGF, IL-17, MPIF-1, and IL-2sRa. In another embodiment, said twoor more biomarkers are selected from the group consisting of: leptin,prolactin, OPN and IGF-II.

In one embodiment, the above described methods of monitoring theprogression of cancer comprises comparing the expression of three ormore biomarkers. In one embodiment, the above described methods ofmonitoring the progression of cancer comprises comparing the expressionof four or more biomarkers. In one embodiment, the above describedmethods of monitoring the progression of cancer comprises comparing theexpression of four or more biomarkers, wherein said four or morebiomarkers include leptin, prolactin, OPN and IGF-II. In anotherembodiment, the above described method of monitoring the progression ofcancer comprises comparing the expression of four biomarkers, whereinthe four biomarkers are leptin, prolactin, OPN and IGF-II.

The invention also comprises methods for monitoring the effectiveness ofa treatment against cancer. In one embodiment, the invention comprise amethod for monitoring the effectiveness of a treatment against cancercomprising comparing the expression of one or more biomarkers in asample from a subject prior to providing at least a portion of atreatment to the expression of said one or more biomarkers in a sampleobtained from the subject after the subject has received at least aportion of the treatment; wherein said one or more biomarkers areselected from the group consisting of: 6Ckine, ACE, BDNF, CA125,E-Selectin, EGF, Eot2, ErbB1, follistatin, HCC4, HVEM, IGF-II, IGFBP-1,IL-17, IL-1srII, IL-2sRa, leptin, M-CSF R, MIF, MIP-1a, MIP3b, MMP-8,MMP7, MPIF-1, OPN, PARC, PDGF Rb, prolactin, ProteinC, TGF-b RIII,TNF-R1, TNF-a, VAP-1, VEGF R2 and VEGF R3; and wherein a difference inthe expression of said one or more biomarker diagnoses or aids in thediagnosis of the efficacy of the treatment. In one embodiment, said oneor more biomarkers are selected from the group consisting of: prolactin,MIF, OPN, IGF-II, E-Selectin, leptin, EGF, IL-17, MPIF-1, and IL-2sRa.In one embodiment, said one or more biomarkers are selected from thegroup consisting of: leptin, prolactin, OPN and IGF-II.

In one embodiment, the above described methods of monitoring theeffectiveness of a treatment against cancer comprises comparing theexpression of two or more biomarkers. In one embodiment, said two ormore biomarkers are selected from the group consisting of: prolactin,MIF, OPN, IGF-II, E-Selectin, leptin, EGF, IL-17, MPIF-1, and IL-2sRa.In another embodiment, said two or more biomarkers are selected from thegroup consisting of: leptin, prolactin, OPN and IGF-II.

In one embodiment, the above described methods of monitoring theeffectiveness of a treatment against cancer comprises comparing theexpression of three or more biomarkers. In one embodiment, the abovedescribed methods of monitoring the effectiveness of a treatment againstcancer comprises comparing the expression of four or more biomarkers. Inone embodiment, the above described methods of monitoring theeffectiveness of a treatment against cancer comprises comparing theexpression of four or more biomarkers, wherein said four or morebiomarkers include leptin, prolactin, OPN and IGF-II. In anotherembodiment, the above described method of monitoring the effectivenessof a treatment against cancer comprises comparing the expression of fourbiomarkers, wherein the four biomarkers are leptin, prolactin, OPN andIGF-II.

The invention also comprises kits for diagnosing or aiding in thediagnosis of cancer and kits for monitoring cancer. In one embodiment,the kit comprises: (i) a receptacle for receiving a sample; (ii) one ormore reagents for detecting one or more biomarkers selected from thegroup consisting of: 6Ckine, ACE, BDNF, CA125, E-Selectin, EGF, Eot2,ErbB1, follistatin, HCC4, HVEM, IGF-II, IGFBP-1, IL-17, IL-1srII,IL-2sRa, leptin, M-CSF R, MIF, MIP-1a, MIP3b, MMP-8, MMP7, MPIF-1, OPN,PARC, PDGF Rb, prolactin, ProteinC, TGF-b RIII, TNF-R1, TNF-a, VAP-1,VEGF R2 and VEGF R3; and (iii) a reference sample. In one embodiment,the kit comprises one or more reagents for the detection of leptin,prolactin, OPN and IGF-II.

The invention also comprises a method to screen for a candidate compounduseful to treat cancer. In one embodiment, the invention comprises amethod to screen for a candidate compound useful to treat cancercomprising: (i) identifying a candidate compound which regulates theexpression of at least one biomarker selected from the group consistingof: 6Ckine, ACE, BDNF, CA125, E-Selectin, EGF, Eot2, ErbB1, follistatin,HCC4, HVEM, IGF-II, IGFBP-1, IL-17, IL-1srII, IL-2sRa, leptin, M-CSF R,MIF, MIP-1a, MIP3b, MMP-8, MMP7, MPIF-1, OPN, PARC, PDGF Rb, prolactin,ProteinC, TGF-b RIII, TNF-R1, TNF-a, VAP-1, VEGF R2 and VEGF R3; and(ii) determining whether such candidate compound is effective to treatcancer. In one embodiment, the method comprises identifying a candidatecompound which regulates the expression of at least one biomarkersselected from the group consisting of: prolactin, MIF, OPN, IGF-II,E-Selectin, leptin, EGF, IL-17, MPIF-1, and IL-2sRa. In one embodiment,the method comprises identifying a candidate compound which regulatesthe expression of at least one biomarkers selected from the groupconsisting of leptin, prolactin, OPN and IGF-II.

The invention also comprises a method of conducting a business. In oneembodiment, the method of conducting a business comprises: (i) obtaininga sample; (ii) detecting the expression of one or more biomarker in thesample, wherein said one or more biomarkers are selected from the groupconsisting of: 6Ckine, ACE, BDNF, CA125, E-Selectin, EGF, Eot2, ErbB1,follistatin, HCC4, HVEM, IGF-II, IGFBP-1, IL-17, IL-1srII, IL-2sRa,leptin, M-CSF R, MIF, MIP-1a, MIP3b, MMP-8, MMP7, MPIF-1, OPN, PARC,PDGF Rb, prolactin, ProteinC, TGF-b RIII, TNF-R1, TNF-a, VAP-1, VEGF R2and VEGF R3; and (iii) reporting the results of such detection. In oneembodiment, said one or more biomarkers are selected from the groupconsisting of: prolactin, MIF, OPN, IGF-II, E-Selectin, leptin, EGF,IL-17, MPIF-1, and IL-2sRa. In another embodiment, said one or morebiomarkers are selected from the group consisting of: leptin, prolactin,OPN and IGF-II.

In one embodiment, the invention comprises a method of conducting abusiness comprising: (i) obtaining a sample; (ii) detecting theexpression of four biomarkers in the sample, wherein said fourbiomarkers leptin, prolactin, OPN and IGF-II; and (iii) reporting theresults of such detection.

The invention also comprises methods to screen for candidate cancerbiomarkers. In one embodiment, the invention comprises a method toscreen for candidate cancer biomarkers comprising: (i) identifying agroup of biomarkers that are potentially associated with cancer; (ii)comparing the level of expression of the biomarkers identified in step(i) in a first population of cancer subjects and in healthy subjects;(iii) selecting biomarkers exhibiting a significant difference inexpression in said first population of cancer subjects; (iv) comparingthe level of expression of the biomarkers identified in step (iii) in asecond population of cancer subjects and in healthy subjects; and (v)selecting biomarkers exhibiting a significant difference in expressionin said second population of cancer subjects; wherein the biomarkersidentified in step (v) are candidate cancer biomarkers. In oneembodiment, said first population of cancer subjects have newlydiagnosed cancer, and said second population of cancer subjects haverecurrent cancer. In one embodiment, said first population of cancersubjects have recurrent cancer and said second population of cancersubjects have newly diagnosed cancer. In another embodiment, whereinsaid first population of cancer subjects have late stage cancer and saidsecond population of cancer subjects have early stage cancer. In anotherembodiment, said first population of cancer patients have early stagecancer and said second population of cancer subjects have later stagecancer. In another embodiment, said method further comprises: (vi)comparing the level of expression of the biomarkers identified in step(v) in a third population of cancer subjects and in healthy subjects,wherein the expression of said biomarkers is detected by using adifferent assay format; and (vii) selecting biomarkers exhibiting asignificant different in expression in said third population of cancersubjects; wherein the biomarkers identified in step (vii) are candidatebiomarkers for cancer. In one embodiment, said method further comprisesdetermining whether the biomarkers identified in step (v) or (vii) coulddistinguish between cancer and healthy subjects in a blind study.

The invention also comprises a method to screen for candidate cancerbiomarkers comprising: (i) identifying a cancer biomarker; (ii)selecting polypeptides which regulate or are regulated by the biomarkeridentified in step (i); and (iii) measuring the expression of thepolypeptides identified in step (ii) in cancer subjects and in healthysubjects, wherein a polypeptide which is differentially expressed incancer subjects and in healthy subjects is a candidate cancer biomarker.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of the screening process used toidentify biomarkers which could discriminate between subjects withcancer and healthy subjects.

FIG. 2 is a schematic representation of a sample protein microarrayslide with 16 subarrays. Subarrays refer to the 16 wells, or circularanalysis sites, on the slide. Array refers to the antibody contentprinted in a well. Each microarray slide contains only one type ofarray.

FIG. 3 shows the difference in expression of four proteins (leptin,prolactin, OPN and IGF-II) in subjects with ovarian cancer and inhealthy subjects using two different assays: RCA microarray immunoassayand ELISA.

FIG. 4 shows results of analysis of the expression data of four proteins(leptin (identified as “1”), prolactin (identified as “2”), OPN(identified as “3”) and IGF-II (identified as “4”)) in 206 subjects,using the least square fit in a traditional binary data set analysis.The protein levels of healthy subjects are shown in black (left) andthose for subjects with ovarian cancer are shown in gray (right)

FIG. 5 shows results of analysis of the expression data of four proteins(leptin (identified as “1”), prolactin (identified as “2”), OPN(identified as “3”) and IGF-II (identified as “4”)) in 206 subjects,using pair plots. The data points derived from healthy subjects are inblack and the data points derived from subjects with ovarian cancer arein gray.

FIG. 6 shows the scores assigned to 206 subjects including 106 healthysubjects and 100 subjects with ovarian cancer based on the score-basedclassification system described herein. Subjects having a score greaterthan or equal to 2 can be diagnosed with ovarian cancer, while subjectswith score less than or equal to 1 can be diagnosed as free of ovariancancer. The data points derived from healthy subjects are in light grayand the data points derived from subjects with ovarian cancer are indark gray.

DETAILED DESCRIPTION OF THE INVENTION

I. Overview

Described herein is a method which can be used to discriminate betweencancer subjects (including subjects diagnosed with early stage (stageI-II) disease) and healthy subjects. This method is based on theidentification of biomarkers which are particularly well suited todiscriminate between cancer subjects and healthy subjects. Thesebiomarkers were identified using a unique and novel screening methoddescribed herein involving several different screening steps usingsamples from different subjects in each step and validation withdifferent techniques. The biomarkers disclosed herein can be used in thediagnosis, prognosis and monitoring of cancer.

In one particular embodiment, the invention disclosed herein refers to anew test based on four biomarkers: leptin, prolactin, ORN and IGF II,which discriminate between cancer subjects and healthy subjects,particularly between ovarian cancer subjects and healthy subjects. Inone embodiment, these four biomarkers can be used in a blood test forthe diagnosis, prognosis and monitoring of ovarian cancer.

These biomarkers identified herein can be used in combination withadditional known biomarkers. For example, a known biomarker of ovariancancer is CA125. The use of CA125 in conjunction with the biomarkersidentified herein presents a novel approach for the early detection ofovarian cancer and may significantly improve our ability to accuratelydetect pre-malignant change or early stage ovarian cancer inasymptomatic women at increased risk for the development of ovariancancer. Further, the biomarkers identified in this application can beused in conjunction with other diagnostic techniques. For example, forthe diagnosis of ovarian cancer, the biomarkers identified in thisapplication can be used in conjunction with vaginal examination,ultrasound or MRI to diagnose ovarian cancer.

The articles “a,” “an” and “the” are used herein to refer to one or tomore than one (i.e., to at least one) of the grammatical object of thearticle.

The term “including” is used herein to mean, and is used interchangeablywith, the phrase “including but not limited to”.

The term “or” is used herein to mean, and is used interchangeably with,the term “and/or,” unless context clearly indicates otherwise.

The term “such as” is used herein to mean, and is used interchangeablywith, the phrase “such as but not limited to”.

II. Methods of Diagnosis

In one embodiment, the invention refers to a method for diagnosing oraiding in the diagnosis of cancer in a subject. In one embodiment, themethod comprises comparing the expression of one or more biomarkersselected from the group consisting of the biomarkers identified in Table2 in a sample from a subject to a predetermined standard for each saidone or more biomarkers, wherein a significant difference in theexpression of said one or more biomarkers in said sample as compared toa predetermined standard of each said one or more biomarkers diagnosesor aids in the diagnosis of cancer. In one embodiment, said one or morebiomarkers are selected from the group consisting of the biomarkersidentified in Table 3. In another embodiment, said one or morebiomarkers are selected from the group consisting of: leptin, prolactin,OPN and IGF-II.

When the biomarkers are prolactin and/or OPN, an increase in theexpression of said biomarkers as compared to the predetermined standardfor said biomarker diagnoses or aids in the diagnosis of cancer. Whenthe biomarkers is leptin and/or IGF-II, a decrease in the expression ofsaid biomarker as compared to the predetermined standard for saidbiomarker diagnoses or aids in the diagnoses of cancer. As used herein,an increase or decrease in expression refers to the fact that level of agene expression product is made higher or lower, or to the fact that theactivity of the gene expression product is enhanced or lowered.

The above described methods can be used to diagnose any cancer or tumor.In one embodiment, the cancer is ovarian cancer. In another embodiment,the cancer is breast cancer. In another embodiment, the cancer is coloncancer. In another embodiment, the cancer is prostate cancer. In anotherembodiment, the cancer is cervical cancer.

As used herein, the term “biomarker” refers to one or more polypeptidesthat can be used to: diagnose, or to aid in the diagnosis or prognosisof, cancer either alone or as combination of multiple polypeptides;monitor the progression of cancer; and/or monitor the effectiveness of acancer treatment. As used herein, the term “polypeptide” refers to apolymer of amino acids, and not to a specific length. Thus, peptides,oligopeptides and proteins are included within the definition ofpolypeptide.

As used herein, the term “leptin” includes all homologs, naturallyoccurring allelic variants, isoforms and precursors of leptin. Leptin isalso known as HGNC:6553, OB, OBS, obesity, or murine obesity homolog. Inone embodiment, leptin comprises the amino acid sequence of GenBankAccession No. NP_000221.

As used herein, the term “prolactin” includes all homologs, naturallyoccurring allelic variants, isoforms and precursors of prolactin.Prolactin is also known as PRL or HGNC:9445. In one embodiment,prolactin comprises the amino acid sequence of GenBank Accession No.NP_000939.

As used herein, the term “OPN” includes all homologs, naturallyoccurring allelic variants, isoforms and precursors of OPN. OPN is alsoknown as HGNC:11255, BNSP, BSPI, ETA-1, secreted phosphoprotein-1 orosteopontin. In one embodiment, OPN comprises the amino acid sequence ofGenBank Accession No. NP_000573.

As used herein, the term “IGF-II” includes all homologs, naturallyoccurring allelic variants, isoforms and precursors of IGF-II. IGF-II isalso known as HGNC:5466, insulin-like growth factor 2, insulin-likegrowth factor II or somatomedin A. In one embodiment, IGF-II comprisesthe amino acid sequence of GenBank Accession No. NP_000603.

As used herein, the term “subject” or “patient” includes allwarm-blooded animals. In one embodiment the subject is a human. In oneembodiment, the subject is a subject with an enhanced risk of developingcancer.

In one embodiment, when the method relates to ovarian cancer, thesubject is a female (such as a woman) suspected of having or known tohave ovarian cancer, or with an enhanced risk of developing ovariancancer. For example, for ovarian cancer subjects having a familialhistory of ovarian cancer, subjects identified as having a mutantoncogene, and subjects at least about 50 years of age have an enhancedrisk of developing ovarian cancer.

As used herein, the term “sample” refers to a material obtained from asubject. The sample can be derived from any biological source, includingall body fluids (such as, for example, whole blood, plasma, serum,saliva, ocular lens fluid, sweat, urine, milk, etc.), tissue orextracts, cells, etc. Examples of ovary-associated body fluids includeblood fluids (e.g. whole blood, blood serum, blood having plateletsremoved therefrom, etc.), lymph, ascitic fluids, gynecological fluids(e.g. ovarian, fallopian, and uterine secretions, menses, vaginaldouching fluids, fluids used to rinse ovarian cell samples, etc.),cystic fluid, urine, and fluids collected by peritoneal rinsing (e.g.fluids applied and collected during laparoscopy or fluids instilled intoand withdrawn from the peritoneal cavity of a human patient).

The term “expression” is used herein to mean the process by which apolypeptide is produced from DNA. The process involves the transcriptionof the gene into mRNA and the translation of this mRNA into apolypeptide. Depending on the context in which used, “expression” mayrefer to the production of RNA, protein or both.

Expression of a biomarker of the invention may be assessed by any of awide variety of well known methods for detecting expression of atranscribed molecule or its corresponding protein. Non-limiting examplesof such methods include immunological methods for detection of secretedproteins, protein purification methods, protein function or activityassays, nucleic acid hybridization methods, nucleic acid reversetranscription methods, and nucleic acid amplification methods. In apreferred embodiment, expression of a marker gene is assessed using anantibody (e.g. a radio-labeled, chromophore-labeled,fluorophore-labeled, or enzyme-labeled antibody), an antibody derivative(e.g. an antibody conjugated with a substrate or with the protein orligand of a protein-ligand pair {e.g. biotin-streptavidin}), or anantibody fragment (e.g. a single-chain antibody, an isolated antibodyhypervariable domain, etc.) which binds specifically with a proteincorresponding to the marker gene, such as the protein encoded by theopen reading frame corresponding to the marker gene or such a proteinwhich has undergone all or a portion of its normal post-translationalmodification. In another preferred embodiment, expression of a markergene is assessed by preparing mRNA/cDNA (i.e. a transcribedpolynucleotide) from cells in a patient sample, and by hybridizing themRNA/cDNA with a reference polynucleotide which is a complement of apolynucleotide comprising the marker gene, and fragments thereof. cDNAcan, optionally, be amplified using any of a variety of polymerase chainreaction methods prior to hybridization with the referencepolynucleotide; preferably, it is not amplified.

As used herein, a “predetermined standard” for a biomarker refers to thelevels of expression of said biomarker in healthy subjects or theexpression levels of said biomarker in non-cancerous tissue from thesame subject. The predetermined standard expression levels for a givenbiomarker can be established by prospective and/or retrospectivestatistical studies using only routine experimentation. Saidpredetermined standard expression levels can be determined by a personhaving ordinary skill in the art using well known methods.

The term “healthy subject” refers to a subject has not been diagnosedwith cancer or who has not been diagnosed with cancer of the type whichis being analyzed. Thus, for example, in a method to diagnose ovariancancer, a “healthy subject” refers to a subject who does cancer or whodoes not have ovarian cancer.

As used herein, the term “significant difference” is well within theknowledge of a skilled artisan and will be determined empirically withreference to each particular biomarker. For example, a significantdifference in the expression of a biomarker in a subject with cancer ascompared to a healthy subject is any difference in expression which isstatistically significant.

In one embodiment, the method comprises comparing the expression of twoor more biomarkers and the diagnosis of cancer is based on a score-basedclassification method. In one embodiment, the score-based classificationsystem is a based on binary numbers. In one embodiment, the score-basedclassification system comprises determining the expression of mdifferent biomarkers; wherein each biomarker is assigned a score of 0 or1, wherein a biomarker is assigned a score of 0 if the expression ofsaid biomarker is not significantly different from the expression ofsaid biomarker in a predetermined standard and wherein a biomarker isassigned a score of 1 if the expression of said biomarker issignificantly different from the expression of said biomarker in apredetermined standard; wherein the subject is assigned an overall scorewhich corresponds to the sum of the assigned scores from m differentmarkers; and wherein a given threshold (t) is used to diagnose or aid inthe diagnosis of cancer.

In one embodiment, the score-based classification system comprisescomparing the expression of four (4) different biomarkers; wherein eachbiomarker is assigned a score of 0 or 1, wherein a biomarker is assigneda score of 0 if the expression of said biomarker is not significantlydifferent from the expression of said biomarker in a predeterminedstandard and wherein a biomarker is assigned a score of 1 if theexpression of said biomarker is significantly different from theexpression of said biomarker in a predetermined standard; wherein thesubject is assigned an overall score which corresponds to the sum of theassigned scores from four (4) different markers; and wherein a score or2 or more diagnoses or aids in the diagnosis of cancer. In oneembodiment, the four biomarkers are leptin, prolactin, OPN and IGF-II.

In one embodiment, the method comprises comparing the expression of twoor more biomarkers, wherein the diagnosis of cancer is made by comparingthe expression profile of said two or more biomarkers to a predeterminedstandard profile for said biomarkers, and wherein a difference in theprofiles diagnoses or aids in the diagnosis of cancer. As used herein,an “expression profile” is a representation of the levels of expressionof one or more biomarkers in a given sample.

In one embodiment, the predetermined standard profile is determined bycomparing the expression of said two or more biomarkers in cancersubjects to the expression of said two or more biomarkers in healthysubjects using a machine learning technique. In one embodiment, thepredetermined standard profile is determined by comparing the expressionof said two or more biomarkers in cancer subjects and in healthysubjects using support vector machines, K-nearest neighbor classifier,or classification tree analysis.

In one embodiment, the method comprises detecting an additional knownbiomarker which is not identified in Table 2 and comparing theexpression of said additional known biomarker to a predeterminedstandard for said additional known biomarker. Additional biomarkers forcancer can be identified by a person having ordinary skill in the art byreference to the published literature. In one embodiment, the cancer isovarian cancer, and the additional biomarker for ovarian cancer isselected from the group consisting of: human stratum corneumchymotryptic enzyme (HSCCE), kallikrein 4, kallikrein 5, kallikrein 6(protease M), kallikrein 8, kallikrein 9, kallikrein 10, CA125, CA15-3,CA19-9, OVX1, lysophosphatidic acid (LPA), carcinoebryonic antigen(CEA), macrophage colony-stimulating factor (M-CSF), prostasin, CA54-61,CA72, HMFG2, IL-6, IL-10, LSA, M-CSF, NB70K, PLAP, TAG72, TNF, TPA,UGTF, WAP four-disulfide core domain 2 (HE4), matrix metalloprotease 2,tetranectin, inhibin, mesothelyn, MUC1, VEGF, CLDN3, NOTCH3, E2Ftranscription factor 3 (E2F3), GTPase activating protein (RACGAP1),hemotological and neurological expressed 1 (HN1), apolipoprotein A1,laminin, claudin 3, claudin 4, tumor-associated calcium signaltransducer 1 (TROP-1/Ep-CAM), tumor-associated calcium signal transducer2 (TROP-2), ladinin 1, S100A2, SERPIN2 (PAI-2), CD24, lipocalin 2,matriptase (TADG-15), stratifin, transforming growth factor-betareceptor III, platelet-derived growth factor receptor alpha, SEMACAP3,ras homology gene family member I (ARHI), thrombospondin 2,disabled-2/differentially expressed in ovarian carcinoma 2 (Dab2/DOC2),and haptoglobin-alpha subunit. In another embodiment, the additionalbiomarker for ovarian cancer is the truncated form of transthyretin orthe cleavage fragment of inter-alpha-trypsin inhibitor heavy chain H4identified by Zhang et al., Cancer Res. 64(16):5882-90 (2004). In apreferred embodiment, the additional biomarker for ovarian cancer isCA125.

In one embodiment, the invention refers to a method for diagnosing oraiding in the diagnosis of cancer in a subject comprising comparing theexpression of two or more biomarkers selected from the group consistingof the biomarkers identified in Table 2 in a sample from a subject to apredetermined standard for each said biomarker, wherein a significantdifference in the expression of one or more biomarkers in said sample ascompared to a predetermined standard of each biomarker diagnoses or aidsin the diagnosis of cancer. In one embodiment, said two or morebiomarkers are selected from the group consisting of the biomarkersidentified in Table 3. In another embodiment, said two or morebiomarkers are selected from the group consisting of: leptin, prolactin,OPN and IGF-II. In one embodiment, a significant difference in theexpression of at least two of said two or more biomarkers diagnoses oraids in the diagnosis of cancer.

In one embodiment, the invention comprises to a method for diagnosing oraiding in the diagnosis of cancer in a subject comprising comparing theexpression of three or more biomarkers selected from the groupconsisting of the biomarkers identified in Table 2 in a sample from asubject to a predetermined standard for each biomarker, wherein asignificant difference in the expression of one or more biomarkers insaid sample as compared to a predetermined standard of each saidbiomarker diagnoses or aids in the diagnosis of cancer. In oneembodiment, said three or more biomarkers are selected from the groupconsisting of the biomarkers identified in Table 3. In anotherembodiment, said three or more biomarkers are selected from the groupconsisting of: leptin, prolactin, OPN and IGF-II. In one embodiment, asignificant difference in the expression of at least two or said two ormore biomarkers diagnoses or aids in the diagnosis of cancer.

In one embodiment, the invention refers to a method for diagnosing oraiding in the diagnosis of cancer in a subject comprising comparing theexpression of four or more biomarkers selected from the group consistingof the biomarkers identified in Table 2 in a sample from a subject to apredetermined standard for each biomarker, wherein a significantdifference in the expression of one or more biomarkers in said sample ascompared to a predetermined standard of each said biomarker diagnoses oraids in the diagnosis of cancer. In one embodiment, said four or morebiomarkers are selected from the group consisting of the biomarkersidentified in Table 3. In another embodiment, said four or morebiomarkers are selected from the group consisting of: leptin, prolactin,OPN and IGF-II. In one embodiment, a significant difference in theexpression of at least two of said two or more biomarkers diagnoses oraids in the diagnosis of cancer.

The expression of said one or more biomarkers can be detected using anymethod known to a person having ordinary skill in the art. In oneembodiment, the expression of said one or more biomarkers can bedetected using a reagent that detects said one or more biomarkers. Saidreagent can be any reagent that specifically detects said one or morebiomarkers. Said reagent can be an antibody (natural or synthetic) or afragment thereof specific for the biomarker, a peptide, a nucleic acid,or any other reagent that can specifically detect a biomarker. As usedherein, the term “antibody” includes chimeric and synthetic antibodies,e.g., generated by combinatorial mutagenesis and phage display. The term“antibody” includes mimetics or peptidomimetics of antibodies.Peptidomimetics are compounds based on, or derived from, peptides andproteins. The peptidomimetics of the present invention typically can beobtained by structural modification of a known peptide sequence usingunnatural amino acids, conformational restraints, isosteric replacement,and the like. The subject peptidomimetics constitute the continuum ofstructural space between peptides and non-peptide synthetic structures;peptidomimetics may be useful, therefore, in delineating pharmacophoresand in helping to translate peptides into non-peptide compounds with theactivity of the parent peptides. For illustrative purposes, peptideanalogs of the antibodies can be generated using, for example,benzodiazepines (e.g., see Freidinger et al. in Peptides: Chemistry andBiology, G. R. Marshall ed., ESCOM Publisher: Leiden, Netherlands,1988), substituted gamma lactam rings (Garvey et al. in Peptides:Chemistry and Biology, G. R. Marshall ed., ESCOM Publisher: Leiden,Netherlands, 1988, p 123), C-7 mimics (Huffman et al. in Peptides:Chemistry and Biology, G. R. Marshall ed., ESCOM Publisher: Leiden,Netherlands, 1988, p. 105), keto-methylene pseudopeptides (Ewenson etal. (1986) J Med Chem 29:295; and Ewenson et al. in Peptides: Structureand Function (Proceedings of the 9^(th) American Peptide Symposium)Pierce Chemical Co. Rockland, Ill., 1985), β-turn dipeptide cores (Nagaiet al. (1985) Tetrahedron Lett 26:647; and Sato et al. (1986) J Chem SocPerkin Trans 1:1231), β-aminoalcohols (Gordon et al. (1985) BiochemBiophys Res Commun 126:419; and Dann et al. (1986) Biochem Biophys ResCommun 134:71), diaminoketones (Natarajan et al. (1984) Biochem BiophysRes Commun 124:141), and methyleneamino-modified (Roark et al. inPeptides: Chemistry and Biology, G. R. Marshall ed., ESCOM Publisher:Leiden, Netherlands, 1988, p 134). Also, see generally, Session III:Analytic and synthetic methods, in Peptides: Chemistry and Biology, G.R. Marshall ed., ESCOM Publisher: Leiden, Netherlands, 1988).

In another embodiment, said reagent is directly or indirectly labeledwith a detectable substance. The detectable substance may be, forexample, selected, e.g., from a group consisting of radioisotopes,fluorescent compounds, enzymes, and enzyme co-factor. Methods oflabeling antibodies are well known in the art.

As used herein, the term “detect”, “detected” or “detecting” includesmeasure, measured or measuring.

The above described methods can be performed using any sample. In oneembodiment, the sample is a body fluid sample. In one embodiment, thebody fluid sample is blood or serum.

In another embodiment, the expression of said one or more biomarkers aredetected using mass spectroscopy.

In yet another embodiment, the expression of said one or more biomarkersis detected by detecting the mRNA transcription levels of the geneencoding said at one or more biomarker.

In yet another embodiment, the expression of said one or more biomarkerscan be detected by ELISA, RCA immunoassay, chemiluminescence, thin-filmoptical biosensor, proton resonance technology, protein microarray assayor any other detection method known in the art.

In one embodiment, the expression of said one or more biomarkers aredetected by: (a) detecting the expression of a polypeptide which isregulated by said one or more biomarker; (b) detecting the expression ofa polypeptide which regulates said biomarker; or (c) detecting theexpression of a metabolite of said biomarker. A person of skill in theart would be able to identify polypeptides which regulate or areregulated by a biomarker, and metabolites of a biomarker, using onlyroutine experimentation.

The above described methods to diagnose or aid in the diagnosis ofcancer may be used in conjunction with other methods to validate theresults (i.e. to more conclusively determine whether a subject hascancer). In one embodiment, the cancer is ovarian cancer and the abovedescribed methods further comprise: physical examination, ultrasoundexamination, x-ray examination, MRI examination, laparotomy and/orhematological tests. Hematological tests which may be indicative ofovarian cancer in a patient include analyses of serum levels ofadditional biomarkers of ovarian cancer.

III. Methods of Monitoring

In one embodiment, the invention comprises a method of monitoring theprogression of cancer in a subject comprising comparing the expressionof one or more biomarkers selected from the group consisting of thebiomarkers identified in Table 2 in a sample from a subject; to theexpression of said one or more biomarkers in a sample obtained from thesubject at a subsequent point in time, wherein a difference in theexpression of said one or more biomarkers are indicative of theprogression of the cancer in the subject. In one embodiment, said one ormore biomarkers are selected from the group consisting of the biomarkersidentified in Table 3. In another embodiment, said one or morebiomarkers are selected from the group consisting of leptin, prolactin,OPN and IGF-II.

In one embodiment, the method comprises comparing the expression of twoor more biomarkers. In another embodiment, the method comprisescomparing the expression of three or more biomarkers. In anotherembodiment, the method comprises comparing the expression of four ormore biomarkers. In one embodiment, the method comprises comparing theexpression of four or more biomarkers, wherein said four or morebiomarkers include leptin, prolactin, OPN and IGF-II. In yet anotherembodiment, the method comprises comparing the expression of fourbiomarkers: leptin, prolactin, OPN and IGF-II.

In one embodiment, the method is used to monitor the progression ofcancer after the subject has received a treatment for cancer.

The invention also comprises a method for monitoring the effectivenessof a treatment against cancer, comprising comparing the expression ofone or more biomarkers selected from the group consisting of thebiomarkers identified in Table 3 in a sample from a subject prior toproviding at least a portion of a treatment to the expression of saidone or more biomarkers in a sample obtained from the subject after thesubject has received at least a portion of the treatment, wherein adifference in the expression of said one or more biomarkers areindicative of the efficacy of the treatment.

In one embodiment, said one or more biomarkers are selected from thegroup consisting of the biomarkers identified in Table 3. In anotherembodiment, said one or more biomarkers are selected from the groupconsisting of leptin, prolactin, OPN and IGF-II.

In one embodiment, the method comprises comparing the expression of twoor more biomarkers. In another embodiment, the method comprisescomparing the expression or three or more biomarkers. In anotherembodiment, the method comprises comparing the expression of four ormore biomarkers. In one embodiment, the method comprises comparing theexpression of four or more biomarkers, wherein said four or morebiomarkers include leptin, prolactin, OPN and IGF-II. In yet anotherembodiment, the method comprises comparing the expression of fourbiomarkers: leptin, prolactin, OPN and IGF-II.

It will be appreciated that as used herein, the “treatment” may be anytreatment for treating ovarian cancer including, but not limited to,chemotherapy, immunotherapy, gene therapy, radiation therapy andsurgical removal of tissue. As used herein, “a portion of a treatment”refers to any portion of a treatment for cancer, such as a dose of acompound used to treat cancer, or a portion of a treatment such aschemotherapy.

The above described methods of monitoring cancer are applicable to anycancer or tumor. In one embodiment, the method is for monitoring ovariancancer. In one embodiment, the method is for the monitoring breastcancer. In one embodiment, the method is for monitoring colon cancer. Inanother embodiment, the method is for monitoring cervical cancer.

IV. Kits

The invention also comprises kits for diagnosing or aiding in thediagnosis of cancer or for monitoring cancer. The kits can be used todiagnose or monitor any cancer. In one embodiment, the kit is for thediagnosis or monitoring of ovarian cancer. In one embodiment, the kit isfor the diagnosis or monitoring of breast cancer. In one embodiment, thekit is for the diagnosis or monitoring of colon cancer. In oneembodiment, the kit is for the diagnosis or monitoring of cervicalcancer.

In one embodiment, the kit comprises: (i) a receptacle for receiving asample; (ii) one or more reagents for detecting one or more biomarkersselected from the group consisting of the biomarkers identified in Table2; and (iii) a reference sample. In one embodiment, the kit comprisesone or more reagents for detecting one or more biomarkers selected fromthe group consisting of the biomarkers identified in Table 3. In oneembodiment, the kit comprises one or more reagents for detecting one ormore biomarkers selected from the group consisting of leptin, prolactin,OPN and IGF-II.

In one embodiment, the kit comprises reagents for detecting two or morebiomarkers. In one embodiment, said two or more biomarkers are selectedfrom the group consisting of: leptin, prolactin, OPN and IGF-II.

In another embodiment, said kit comprises reagents for detecting threeor more biomarkers.

In one embodiment, the kit comprises reagents for detecting four or morebiomarkers. In one embodiment, said four or more biomarkers includeleptin, prolactin, OPN and IGF-II.

The reagents may be labeled compounds or agents capable of detecting apolypeptide or an mRNA encoding a polypeptide corresponding to a markergene of the invention in a biological sample and means for determiningthe amount of the polypeptide or mRNA in the sample (e.g., an antibodywhich binds the polypeptide or an oligonucleotide probe which binds toDNA or mRNA encoding the polypeptide). Suitable reagents for bindingwith a polypeptide corresponding to a marker gene of the inventioninclude antibodies, antibody derivatives, antibody fragments, and thelike. Suitable reagents for binding with a nucleic acid (e.g. a genomicDNA, an mRNA, a spliced mRNA, a cDNA, or the like) include complementarynucleic acids.

For antibody-based kits, the kit can comprise, for example: (1) a firstantibody (e.g., attached to a solid support) which binds to apolypeptide corresponding to a marker gene of the invention; and,optionally, (2) a second, different antibody which binds to either thepolypeptide or the first antibody and is conjugated to a detectablelabel.

For oligonucleotide-based kits, the kit can comprise, for example: (1)an oligonucleotide, e.g., a detectably labeled oligonucleotide, whichhybridizes to a nucleic acid sequence encoding a polypeptidecorresponding to a marker gene of the invention or (2) a pair of primersuseful for amplifying a nucleic acid molecule corresponding to a markergene of the invention.

The reference sample is used to compare the results obtained from thesample being tested.

The kit can also comprise other components such as a buffering agent, apreservative, or a protein stabilizing agent. The kit can furthercomprise components necessary for detecting the detectable label (e.g.,an enzyme or a substrate).

Each component of the kit can be enclosed within an individual containerand all of the various containers can be within a single package, alongwith instructions for interpreting the results of the assays performedusing the kit.

V. Screening Methods

The present invention also comprises methods to screen for candidatecompounds useful to treat cancer. In one embodiment, the inventioncomprises a method to screen for a candidate compound useful to treatcancer comprising: (i) identifying a candidate compound which regulatesthe expression of one or more biomarkers selected from the groupconsisting of the biomarkers identified in Table 2; and (ii) determiningwhether such candidate compound is effective to treat cancer. In oneembodiment, said one or more biomarkers are selected from the groupconsisting of the biomarkers identified in Table 3. In anotherembodiment, said one or more biomarkers are selected from the groupconsisting of leptin, prolactin, OPN and IGF-II.

In one embodiment, the invention comprises a method to screen for acandidate compound useful to treat cancer comprising: (i) identifying acandidate compound which regulates the expression of two or morebiomarkers selected from the group consisting of the biomarkersidentified in Table 2; and (ii) determining whether such candidatecompound is effective to treat cancer. In one embodiment, said two ormore biomarkers are selected from the group consisting of the biomarkersidentified in Table 3. In another embodiment, said two or morebiomarkers are selected from the group consisting of leptin, prolactin,OPN and IGF-II.

The present invention also comprises methods to screen for candidatecompounds useful to treat cancer. In one embodiment, the inventioncomprises a method to screen for a candidate compound useful to treatcancer comprising: (i) identifying a candidate compound which regulatesthe expression of three or more biomarkers selected from the groupconsisting of the biomarkers identified in Table 2; and (ii) determiningwhether such candidate compound is effective to treat cancer. In oneembodiment, said three or more biomarkers are selected from the groupconsisting of the biomarkers identified in Table 3. In anotherembodiment, said three or more biomarkers are selected from the groupconsisting of leptin, prolactin, OPN and IGF-II.

The present invention also comprises methods to screen for candidatecompounds useful to treat cancer. In one embodiment, the inventioncomprises a method to screen for a candidate compound useful to treatcancer comprising: (i) identifying a candidate compound which regulatesthe expression of four or more biomarkers selected from the groupconsisting of the biomarkers identified in Table 2; and (ii) determiningwhether such candidate compound is effective to treat cancer. In oneembodiment, said four or more biomarkers are selected from the groupconsisting of the biomarkers identified in Table 3. In anotherembodiment, said four or more biomarkers include leptin, prolactin, OPNand IGF-II.

As used herein, the term “compound” refers to any chemical entity,pharmaceutical, drug, and the like that can be used to treat or preventa disease, illness, conditions, or disorder of bodily function.Compounds comprise both known and potential therapeutic compounds. Acompound can be determined to be therapeutic by screening using thescreening methods of the present invention. Examples of test compoundsinclude, but are not limited to peptides, polypeptides, syntheticorganic molecules, naturally occurring organic molecules, nucleic acidmolecules, and combinations thereof.

The above described screening methods can be used to screen forcandidate compounds useful to treat any cancer. In one embodiment, themethod is to screen for candidate compounds useful to treat ovariancancer. In another embodiment, the method is to screen for candidatecompounds useful to treat breast cancer. In another embodiment, themethod is to screen for candidate compounds useful to treat coloncancer. In another embodiment, the method is to screen for candidatecompounds useful to treat cervical cancer.

VI. Business Methods

The invention further comprises a method of conducting a businesscomprising: (i) obtaining a sample; (ii) detecting the expression of atleast one biomarker in the sample, wherein said one or more biomarker isselected from the group consisting of the biomarkers identified in Table2; and (iii) reporting the results of such detection. In one embodiment,said one or more biomarkers are selected from the group consisting ofthe biomarkers identified in Table 3. In another embodiment, said one ormore biomarkers are selected from the group consisting of leptin,prolactin, OPN and IGF-II.

The invention further comprises a method of conducting a businesscomprising: (i) obtaining a sample; (ii) detecting the expression ofleptin, prolactin, OPN and IGF-II; and (iii) reporting the results ofsuch detection.

VII. General Screening Methods

The invention also comprises a method to screen for candidate cancerbiomarkers comprising: (i) identifying a group of biomarkers that arepotentially associated with cancer (such as oncogenes, tumor suppressorgenes, growth factor-like genes, protease-like genes, and proteinkinase-like genes); (ii) comparing the level of expression of thebiomarkers identified in step (i) in a first population of cancersubjects and in healthy subjects; (iii) selecting biomarkers exhibitinga significant difference in expression in said first population ofcancer subjects; (iv) comparing the level of expression of thebiomarkers identified in step (iii) in a second population of cancersubjects and in healthy subjects; and (v) selecting biomarkersexhibiting a significant difference in expression in said secondpopulation of cancer subjects; wherein the biomarkers identified in step(v) are candidate cancer biomarkers. The first population of cancersubjects and the second population of cancer subjects may be any twocancer populations so long as the two populations are different. In oneembodiment, said first population of cancer subjects consists ofsubjects newly diagnosed with cancer, and said second population ofcancer subjects consists of subjects having recurrent cancer. In anotherembodiment, said first population of cancer subjects consists ofsubjects having later stage cancer and said second population of cancersubjects consists of subjects having early stage cancer; or where saidfirst population of cancer patients consists of subjects having earlystage cancer and said second population of cancer subjects consists ofsubjects having later stage cancer.

A person of skill in the art would be able to identify biomarkers whichare potentially associated with cancer. Such biomarkers can be selectedfrom the group consisting of as oncogenes, tumor suppressor genes,growth factor-like genes, protease-like genes, and protein kinase-likegenes.

In one embodiment, the method further comprises: (vi) comparing thelevel of expression of the biomarkers identified in step (v) in a thirdpopulation of cancer subjects and in healthy subjects, wherein theexpression of said biomarkers is detected by using a different assayformat; and (vi) selecting biomarkers exhibiting a significant differentin expression in said third population of cancer patients; wherein thebiomarkers identified in step (vii) are candidate biomarkers for cancer.Thus, for example, in one embodiment, the expression of said biomarkeris first detected using a high throughput assay, and then detected usingan assay that is specific for the protein in question. For example, inone embodiment, the expression of said biomarker is first detected byusing RCA microarray immunoassay and then detected by ELISA assay. Thethird population of cancer subjects may be the same or different fromthe first and second population of cancer subjects.

In one embodiment, the method further comprises determining whether thebiomarkers identified in step (v) or (vii) could distinguish betweencancer and healthy subjects in a blind study. The results of the blindassay can be analyzed using well known statistical methods.

The expression of said biomarkers can be compared using any method knownin the art. In one embodiment, the expression of the biomarkers isdetected using protein array, mass spectroscopy, gel electrophoresis oran immunoassay. In one embodiment, the expression of the biomarkers isdetected using RCA microarray immunoassay. In another embodiment, theexpression of the biomarkers is measured using ELISA. These methods arewell known in the art.

The invention also comprise a method to screen for candidate cancerbiomarkers comprising: (i) identifying a cancer biomarker; (ii)selecting polypeptides which regulate or are regulated by the biomarkeridentified in step (i); and (iii) measuring the expression of thepolypeptides identified in step (ii) in cancer subjects and in healthysubjects, wherein a polypeptide which is differentially expressed incancer subjects and in healthy subjects is a candidate cancer biomarker.

The above described screening methods can be used to screen forcandidate biomarkers of any cancer. In one embodiment, the method is toscreen for candidate compounds useful to treat ovarian cancer. Inanother embodiment, the method is to screen for candidate biomarkers ofbreast cancer. In another embodiment, the method is to screen forcandidate biomarkers of colon cancer. In another embodiment, the methodis to screen for candidate biomarkers of cervical cancer.

EXEMPLIFICATION Example 1: The Identification of Biomarkers of OvarianCancer

FIG. 1 is a schematic representation of the novel screening assay usedto identify biomarkers of ovarian cancer which can be used todistinguish subjects with ovarian cancer and healthy subjects. As shownin FIG. 1, during Phase I of the screening method, the levels ofexpression of 169 proteins were measured in 46 serum samples (18 sampleswere obtained from subjects with ovarian cancer and 28 samples wereobtained from healthy, age-matched controls) via RCA immunoassaymicroarray in order to identify proteins that are differentiallyexpressed in subjects with ovarian cancer and in healthy subjects.

TABLE 1 Proteins (analytes) used to screen for biomarkers of ovariancancer. (As used herein, the tern “analyte” refers to a molecule orcompound, such as a polypeptide or nucleic acid, whose presence is to beidentified in a sample.) Protein (abbr.) Protein (full name) Array 1analytes 1 ANG Angiogenin 2 BLC (BCA-1) B-lymphocyte chemoattractant 3EGF Epidermal growth factor 4 ENA-78 Epithelial cell-derived neutrophil-activating peptide 5 Eot Eotaxin 6 Eot-2 Eotaxin-2 7 Fas Fas (CD95) 8FGF-7 Fibroblast growth factor-7 9 FGF-9 Fibroblast growth factor-9 10GDNF Glial cell line derived neurotrophic factor 11 GM-CSF Granulocytemacrophage colony stimulating factor 12 IL-1ra Interleukin 1 receptorantagonist 13 IL-2 sRα Interleukin 2 soluble receptor alpha 14 IL-3Interleukin 3 15 IL-4 Interleukin 4 16 IL-5 Interleukin 5 17 IL-6Interleukin 6 18 IL-7 Interleukin 7 19 IL-8 Interleukin 8 20 IL-13Interleukin 13 21 IL-15 Interleukin 15 22 MCP-2 Monocyte chemotacticprotein 2 23 MCP-3 Monocyte chemotactic protein 3 24 MIP-1α Macrophageinflammatory protein 1 alpha 25 MPIF Myeloid progenitor inhibitoryfactor 1 26 OSM Oncostatin M 27 PlGF Placental growth factor Array 2analytes 1 AR Amphiregulin 2 BDNF Brain-derived neurotrophic factor 3Flt-3 Lig fms-like tyrosine kinase-3 ligand 4 GCP-2 Granulocytechemotactic protein 2 5 HCC4 (NCC4) Hemofiltrate CC chemokine 4 6 I-309I-309 7 IL-1α Interleukin 1 alpha 8 IL-1β Interleukin 1 beta 9 IL-2Interleukin 2 10 IL-17 Interleukin 17 11 MCP-1 Monocyte chemotacticprotein 1 12 M-CSF Macrophage colony stimulating factor 13 MIG Monokineinduced by interferon gamma 14 MIP-1β Macrophage inflammatory protein 1beta 15 MIP-1δ Macrophage inflammatory protein 1 delta 16 NT-3Neurotrophin 3 17 NT-4 Neurotrophin 4 18 PARC Pulmonary andactivation-regulated chemokine 19 RANTES Regulated upon activation,normal T expressed and presumably secreted 20 SCF Stem cell factor 21sgp130 Soluble glycoprotein 130 22 TARC Thymus and activation regulatedchemokine 23 TNF-RI Tumor necrosis factor receptor I 24 TNF-α Tumornecrosis factor alpha 25 TNF-β Tumor necrosis factor beta 26 VEGFVascular endothelial growth factor Array 3 analytes 1 BTC Betacellulin 2DR6 Death receptor 6 3 Fas Lig Fas ligand 4 FGF acid (FGF-1) Fibroblastgrowth factor acidic 5 Fractalkine Fractalkine 6 GRO-β Growth relatedoncogene beta 7 HCC-1 Hemofiltrate CC chemokine 1 8 HGF Hepatocytegrowth factor 9 HVEM Herpes virus entry mediator 10 ICAM-3 (CD50)Intercellular adhesion molecule 3 11 IGFBP-2 Insulin-like growth factorbinding protein 2 12 IL-2 Rγ Interleukin 2 receptor gamma 13 IL-5 Rα(CD125) Interleukin 5 receptor alpha 14 IL-9 Interleukin 9 15 Leptin/OBLeptin 16 L-Selectin (CD62L) Leukocyte selectin 17 MCP-4 Monocytechemotactic protein 4 18 MIP-3β Macrophage inflammatory protein 3 beta19 MMP-7 (total) Matrix metalloproteinase 7 20 MMP-9 Matrixmetalloproteinase 9 21 PECAM-1 (CD31) Platelet endothelial cell adhesionmolecule-1 22 RANK Receptor activator of NF-kappa-B 23 SCF R Stem cellfactor receptor 24 TIMP-1 Tissue inhibitors of metalloproteinases 1 25TRAIL R4 TNF-related apoptosis-inducing ligand receptor 4 26 VEGF-R2Vascular endothelial growth factor receptor 2 (Flk-1/KDR) 27 ST2Interleukin 1 receptor 4 Array 4 analytes 1 ALCAM Activated leukocytecell adhesion molecule 2 β-NGF beta-nerve growth factor 3 CD27 CD27 4CTACK Cutaneous T-cell attracting chemokine 5 CD30 CD30 6 Eot-3Eotaxin-3 7 FGF-2 Fibroblast growth factor-2 (FGF-basic) 8 FGF-4Fibroblast growth factor-4 9 Follistatin Follistatin 10 GRO-γ Growthrelated oncogene gamma 11 ICAM-1 Intercellular adhesion molecule 1 12IFN-γ Interferon gamma 13 IFN-ω Interferon omega 14 IGF-1R Insulin-likegrowth factor I receptor 15 IGFBP-1 Insulin-like growth factor bindingprotein 1 16 IGFBP-3 Insulin-like growth factor binding protein 3 17IGFBP-4 Insulin-like growth factor binding protein 4 18 IGF-IIInsulin-like growth factor II 19 IL-1 sR1 Interleukin 1 soluble receptorI 20 IL-1 sRII Interleukin 1 soluble receptor II 21 IL-10 Rβ Interleukin10 receptor beta 22 IL-16 Interleukin 16 23 IL-2 Rβ Interleukin 2receptor beta 24 I-TAC Interferon gamma-inducible T cell alphachemoattractant 25 Lptn Lymphotactin 26 LT βR lymphotoxin-beta receptor27 M-CSF R Macrophage colony stimulating factor receptor 28 MIP-3αMacrophage inflammatory protein 3 alpha 29 MMP-10 Matrixmetalloproteinase 10 30 PDGF Rα Platelet-derived growth factor receptoralpha 31 PF4 Platelet factor-4 32 sVAP-1 Soluble Vascular AdhesionProtein-1 33 TGF-α Transforming growth factor alpha 34 TIMP-2 Tissueinhibitors of metalloproteinases 2 35 TRAIL R1 TNF-relatedapoptosis-inducing ligand receptor 1 36 VE-cadherin Vascular EndothelialCadherin 37 VEGF-D Vascular endothelial growth factor-D Array 5 analytes1 4-1BB (CD137) 4-1BB 2 ACE-2 Angiotensin I converting enzyme-2 3 AFPAlpha fetoprotein 4 AgRP Agouti-related protein 5 CD141Thrombomodulin/CD141 6 CD40 CD40 7 CNTF Rα Ciliary neurotrophic factorreceptor alpha 8 CRP C-reactive protein 9 D-Dimer D-Dimer 10 E-SelectinE-selectin 11 HCG Human chorionic gonadotrophin 12 IGFBP-6 Insulin-likeGrowth Factor Binding Protein 6 13 IL-12 (p40) Interleukin 12 p40 14IL-18 Interleukin 18 15 LIF Rα (gp190) Leukemia inhibitory factorsoluble receptor alpha 16 MIF Macrophage migration inhibitory factor 17MMP-8 (total) Matrix Metalloproteinase-8 18 NAP-2 Neutrophil ActivatingPeptide 2 19 Neutrophil elastase Neutrophil elastase 20 PAI-IIPlasminogen activator inhibitor-II 21 Prolactin Prolactin 22 Protein CHuman Protein C 23 Protein S Human Protein S 24 P-Selectin P-Selectin 25TSH Thyroid stimulating hormone Array 6 analytes 1 6Ckine 6Ckine 2 ACEAngiotensin converting enzyme 3 CA 125 Cancer antigen 125 4 CNTF Ciliaryneurotrophic factor 5 Endostatin Endostatin 6 Endothelin 3 Endothelin 37 ErbB1 Epidermal growth factor receptor 1 8 ErbB2 Epidermal growthfactor receptor 2 9 FGF R3 (IIIc) Fibroblast growth factor receptor 3IIIc isoform 10 FGF-6 Fibroblast growth factor-6 11 FGF-R3 (IIIb)Fibroblast growth factor receptor 3 IIIb isoform 12 G-CSF Granulocytecolony stimulating factor 13 HB-EGF Heparin-Binding EGF-like GrowthFactor 14 IFN-a Interferon alpha 15 LIF Leukemia inhibitory factor 16MMP-1 Matrix metalloproteinase 1 17 MMP-2 Matrix metalloproteinase 2 18Osteopontin Osteopontin 19 PAI-1 Plasminogen activator inhibitor type 120 PDGF Rb Platelet-derived growth factor receptor beta 21 PEDF Pigmentepithelium-derived factor 22 sVCAM-1 Soluble VCAM-1 23 TGF-b RIIITransforming growth factor beta receptor III 24 Tie-2 Tyrosine kinasewith Ig and EGF homology domains 2 25 uPA Urokinase plasminogenactivator 26 uPAR Urokinase plasminogen activator receptor 27 VEGF R3VEGF receptor 3

From this group of 169 proteins, 35 proteins were identified which weredifferentially expressed between healthy subjects and subjects withovarian cancer (p-values less than 0.05 based on analysis of variancetests (ANOVA)). This data is identified in Table 2.

TABLE 2 Proteins showing a significant (p < 0.05) difference inexpression between subjects with ovarian cancer and healthy subjects.Healthy Subjects Ovarian Cancer Healthy-Ovarian Cancer Protein Mean StdDev N Mean Std Dev N Mean Std Dev EffSize p-value 6Ckine 9.18 0.52 289.67 0.69 51 −0.49 0.64 −0.76 0.001813 ACE 12.09 0.43 28 11.67 0.61 510.42 0.56 0.76 0.001763 BDNF 13.7 0.92 28 12.82 1.21 51 0.88 1.12 0.790.001293 CA125 7.05 0.42 28 11.3 2.45 51 −4.25 1.99 −2.13 <.000001E-Selectin 13.83 0.62 28 13.4 0.76 51 0.44 0.71 0.61 0.011176 EGF 8.411.63 28 10.14 1.57 51 −1.73 1.59 −1.09 0.000015 Eot2 13.12 1.11 28 12.551.2 51 0.57 1.17 0.49 0.04228 ErbB1 11.79 0.39 28 11.36 0.55 51 0.44 0.50.87 0.000383 Follistatin 10.26 0.63 28 10.76 1.02 51 −0.49 0.9 −0.550.0225 HCC4 13.93 0.59 28 14.17 0.45 51 −0.25 0.5 −0.49 0.04178 HVEM8.33 0.67 28 8.75 0.7 51 −0.42 0.69 −0.61 0.011777 IGF-II 13.53 0.46 2813.04 0.53 51 0.49 0.51 0.97 0.000094 IGFBP-1 13.24 1.58 28 13.97 1.3451 −0.73 1.43 −0.51 0.033016 IL-17 8.78 0.56 28 8.24 0.55 51 0.53 0.550.96 0.000105 IL-1srII 9.96 0.6 28 9.48 0.69 51 0.48 0.66 0.72 0.002983IL-2sRa 13.14 0.67 27 13.77 0.57 51 −0.63 0.6 −1.04 0.00004 Leptin 12.771.62 27 10.83 2.78 51 1.94 2.44 0.79 0.00134 M-CSF R 12.98 0.35 28 12.780.37 51 0.19 0.37 0.53 0.027136 MIF 10.75 0.75 28 11.82 0.75 51 −1.070.75 −1.42 <.000001 MIP-1a 6.85 0.69 28 6.45 0.73 51 0.4 0.71 0.560.020757 MIP3b 7.55 0.73 28 7.92 0.8 51 −0.37 0.77 −0.48 0.043303 MMP-813.92 1.03 28 14.53 0.82 51 −0.61 0.9 −0.68 0.004956 MMP7 11.57 0.48 2812 0.58 51 −0.43 0.55 −0.79 0.001262 MPIF-1 9.27 0.6 28 9.9 0.7 51 −0.630.67 −0.94 0.000155 OPN 12.62 0.79 28 13.81 0.69 51 −1.2 0.73 −1.64<.000001 PARC 14.21 0.2 28 14.38 0.23 51 −0.17 0.22 −0.78 0.001318 PDGFRb 10.74 0.97 28 10.13 1.13 50 0.61 1.08 0.56 0.019795 Prolactin 11.010.51 28 11.75 1.12 51 −0.74 0.95 −0.78 0.001445 ProteinC 13.59 0.31 2813.24 0.38 51 0.35 0.36 0.97 0.000089 TGF-b RIII 10.46 1.15 28 11.461.12 51 −1 1.13 −0.88 0.000344 TNF-R1 10.14 1.23 28 10.73 1.18 50 −0.591.2 −0.5 0.039197 TNF-a 7.06 0.97 28 6.3 0.7 51 0.75 0.8 0.94 0.000152VAP-1 14.06 0.28 24 13.78 0.65 44 0.29 0.55 0.52 0.042888 VEGF R2 8.840.38 28 8.59 0.49 51 0.26 0.46 0.56 0.0189 VEGF R3 10 0.55 28 9.51 0.6751 0.49 0.63 0.78 0.001388

The protein (or analytes) identified in Table 2 are also known by othernames, which can be identified by reference to the full name of theprotein as described in Table 1 and by reference to the publishedliterature. One way of identifying other names for the proteinsidentified in Table 2 is by reference to the various NCBI databases,which include GenBank.

These 35 proteins were selected for further characterization with 40serum samples obtained from subjects with recurrent ovarian cancer. Fromthis group of 35 proteins, ten (10) biomarkers exhibited the greatestdifferences in protein expression between subjects with recurrentovarian cancer and healthy subjects. These 10 biomarkers are identifiedin Table 3.

TABLE 3 Proteins showing significant difference in expression betweensubjects with recurrent ovarian cancer and healthy subjects. ProteinBonferroni adjusted p-value Prolactin 3.69E−13 MIF 3.61E−06 OPN 0.00001IGF-II 0.00009 E-Selectin 0.00155 Leptin 0.00249 EGF 0.00382 IL-170.00313 MPIF.1 0.00839 IL.2sRa 0.49340

Of these 10 proteins, some of the proteins that showed the mostpotential for differentiating between not only healthy subjects andsubjects newly diagnosed ovarian cancer, but also between healthysubjects and subjects with recurrent disease, were assayed usingsandwich Enzyme Linked ImmunoSorbent Assay (ELISA) on a small cohort of50 subjects (25 cancer subjects with Stage III/IV ovarian cancer andhaving an average age of 63.4 years and 25 healthy subjects having anaverage age of 57 years). Based on ELISA testing of the original sampleset, EGF, TNFa, and IL-17 did not provide consistent differentiationbetween the cancer and control serum samples. MIF-1 was a promisingmarker but ELISA kits were not reliably available to continue testing.As shown in FIG. 3, four proteins showed perfect correlation between theRCA microarray immunoassays and the ELISA assays. The averageconcentrations of the four biomarkers determined for these samples areshown below in Table 4.

TABLE 4 Average protein levels for each of the four biomarkers (specificfor the ELISA tests used). Comparison of Average Protein Levels in Seraof Healthy vs. Ovarian Cancer Patients Leptin IGF-II (ng/ml) Prolactin(ng/ml) OPN (ng/ml) (ng/ml) Normal Range [7-50] [0-25] [0-19] [450-2500]Healthy 12  1 11 716 Ovarian Cancer  3 40 49 350

As determined experimentally above using a specific ELISA test, thepredetermined standard of leptin is 7-50 ng/ml; the predeterminedstandard of prolactin is 0-10 pg/ml; the predetermined standard of OPNis 0.5-19 pg/ml; and the predetermined standard of IGF-II is 450-2500ng/ml. A person of skill in the art would understand that thepredetermined standard concentration of a biomarker may vary from assayto assay depending on various factors.

A final panel of four biomarkers (leptin, prolactin, OPN and IGF-II)were selected for assay in a blind study consisting of 206 serum sampleswhich included samples from 106 healthy subjects and 100 ovarian cancersubjects Stages I-IV. The characteristics of the subjects used in thisblind study are described in Table 5. The expression of these fourbiomarkers was determined by ELISA.

TABLE 5 Disease Status and Ages Of Patient Population Participating inBlind Study Masked Group (n = 206) Average Age Healthy Women Healthy 6658.4 High-risk 40 57.6 Women with Ovarian Cancer Stage I/II 24 59.5Stage III/IV 76 63

To differentiate between subjects with ovarian cancer and healthysubjects, statistical cluster analysis was performed. Although none ofthe four markers could reliably separate the normal and cancer groupsusing the least squares fit in a traditional binary data set analysis(FIG. 4), pair plots of the four markers showed better separationbetween subjects these groups (FIG. 5).

The combined data for the four biomarkers was analyzed by four differentclassifiers: support vector machines (SVM), k-nearest neighborclassifiers (k-NN), classification trees, and a score-basedclassification system to classify samples from healthy subjects andsamples from subjects with ovarian cancer. Support vector machines(SVM), k-nearest neighbor classifiers (k-NN), classification trees arehierarchical clustering models.

FIG. 6 shows the result of the score based classification system.Particularly, FIG. 6 shows the scores assigned to the 206 subjects whoparticipated in the phase of the screening assay. The scores wereassigned using the following method: For each marker, the best splitpoint to minimize the number of misclassified subjects was found. Thesplit point divides the sample space into two intervals: one for healthyand another for cancer. A score 0 is assigned to a subject if itsrelated observation falls in the normal interval; otherwise, a score 1is assigned. Table 6 shows the split point for each of the fourbiomarkers described above. Overall, an individual is assigned a scoreas the sum of these assigned scores from 4 different markers. Thus, inthis instance the range of such score is [0, 4]. FIG. 6 illustrates thatsubjects having a score greater than or equal to 2 are likely to havecancer; and subjects with a score less than or equal to 1 are likely tobe healthy.

TABLE 6 Scoring Criteria for Biomarkers Biomarker Split point Leftinterval Right interval Leptin (1) 2.5 ng/ml  Cancer (1) Normal (0)Prolactin (2) 10 pg/ml Normal (0) Cancer (1) OPN (3) 21 pg/ml Normal (0)Cancer (1) IGF-II (4) 491 ng/ml  Cancer (1) Normal (0)

Table 7 gives classification results based on 10-fold cross-validationfor all four classification methods considered. The results indicatedthat all the classification methods can well distinguish normal andcancer groups. The proposed score based classification method performedbetter than the nearest neighbor and classification tree methods. Theresults from the scoring method are comparable to those of SVM. Thesensitivity of the test is 96%, specificity 97%, PPV 97% and NPV 96%.The “sensitivity” of an assay refers to the probability that the testwill yield a positive result in an individual afflicted with ovariancancer. The “specificity” of an assay refers to the probability that thetest will yield a negative result in an individual not afflicted withovarian cancer. The “positive predictive value” (PPV) of an assay is theratio of true positive results (i.e. positive assay results for patientsafflicted with ovarian cancer) to all positive results (i.e. positiveassay results for patients afflicted with ovarian cancer+positive assayresults for patients not afflicted with ovarian cancer).

TABLE 7 Classification results based on 10-fold cross-validation.Classification Method False Positive False Negative SVM 3/106 4/100 TREE10/106  7/100 k-NN 6/106 10/100  Score-based 6/106 4/100

Finally, an additional validation blind study was performed on forty(40) samples using the score-based classification system discussedabove. This method was able to accurately classify 38 out of the 40subjects as having ovarian cancer or not (one sample was classified as afalse positive and another sample was classified as a false negative).

Table 8 summarizes the level of four biomarkers identified herein(leptin, prolactin, ODN and IGF-II) and biomarker CA125 in subjectshaving stage I and stage II ovarian cancer who participated in thescreening assays described above (phase IV and V), as determined by theELISA assays described herein. (The patients in bold/italicsparticipated in phase V of the screening assay described herein.)

TABLE 8 Expression levels of biomarkers in patients with Stage I andStage II ovarian cancer. Patient Code Sample Description LeptinProlactin OPN IGF-II CA125 Stage I C4 Stage IC Rec granulosa cell OVCA11 45 10 547 14 C69 Stage I OVCA (Cellular fibroma) 0.2 35 23 484 NDC113 Stage IA OVCA/endo CA 9 70 32 475 6 C114 Stage IA 2 24 22 319 ND

Stage II C6 Stage II Bilateral dysgerminoma 11 63 38 638 51.2 (germ-cellOVCA) C8 Stage II A/11C OVCA 3 30 28 513 57.3 C9 Stage II C clear cellOVCA 3 81 40 553 981 C16 Stage II C malig steroid cell OVCA 1 36 106 38115 C19 Stage II C Papillary serous OVCA 2 10 86 193 C24 Stage II CPapillary serous OVCA 0 37 21 364 122 C328 Stage II C Papillary serousOVCA 1 48 10 302 977 C48 Stage II Bilateral dysgerminoma 10 37 42 894125 (germ-cell OVCA) C59 Stage II A/II C OVCA 1 12 16 421 57.3 C62 StageII C OVCA 0.2 35 23 484 ND C63 Stage II C OVCA 3 81 40 553 981 C70 StageII C borderline OVCA 1 84 21 543 119 C71 Stage II Small cell OVCA 0 28217 303 ND C77 Stage II B serous cell OVCA 5 77 81 230 99.2 C89 Stage IIA OVCA 12 16 13 431 405 C102 Stage II C OV Adenocarcinoma 9 1 32 634C103 Stage II osteogenic sarcoma OVCA 9 27 51 371 46.9 C117 Stage II B/LStage11C 0 119 48 260 634 Papillary serous OVCA C120 Stage II C OVCA 057 41 124 99 C135 Stage II C OVCA 0 78 29 501 173

Materials and Methods Used in Example 1

Microarray Manufacture:

Microarrays were prepared according to Schweitzer et al., Nat Biotech(2002) 20:359. In short, glass slides were cleaned and derivatized with3-cyanopropyltriethoxysilane. The slides were equipped with a Teflonmask, which divided the slide into sixteen 0.65 cm diameter wells orcircular analysis sites called subarrays (FIG. 2). Printing wasperformed with a Perkin-Elmer SpotArray Enterprise non-contact arrayerequipped with piezoelectric tips, which dispense a droplet (˜350 pL) foreach microarray spot. Antibodies were applied at a concentration of 0.5mg/mL at defined positions. Each chip was printed with sixteen copies ofone type of array, either array 1, 2, 3, 4, 5 or 6. (See Table 1.) A setof antibodies was printed with quadruplicate spots in each subarray.After printing, chips were inspected using light microscopy. If thepercentage of missing spots observed was greater than 5%, then the batchfailed and the slides were discarded immediately. For all print runsdescribed herein, 100% of the antibody features and >95% of the biotincalibrators were printed. Microarray chips were validated in concertwith a set of qualified reagents in two ways. First, mixtures of 1-3different cytokines were prepared so as to provide a high intensitysignal and applied to 14 wells on a chip (each well treated with adifferent mixture up to the total complement of detector antibodies) andtwo arrays were used as blank controls. The chips were developed andscanned and the resulting signals were compared to the positional map ofthe particular array. Second, a titration QC for all analytes of aspecified array using known sample matrices was performed. Normal humanserum and heparinized plasma were assayed neat or spiked with purifiedrecombinant cytokines representing all analytes in the array. Spikedmixtures were then titrated down the subarrays of a slide from 9,000pg/mL to 37 pg/mL of spiked cytokine concentrations along with twosubarrays for each un-spiked control sample. The data was quantified andfor every analyte in the array a titration curve was generated toexamine feature intensity behavior as a function of concentration. Takentogether, this data was used to confirm the activity of array featuresand reagent sets.

RCA Microarrray Immunoassay:

Prior to assay, the slides were removed from storage at room temperaturein sealed containers and opened in a humidity controlled chamber(45-55%). Slides were blocked with Seablock (Pierce Chemical Co.),diluted 1:1 with PBS for 1 h at 37° C. in a humidified chamber.Following removal of the blocking solution, they were washed twice with1×PBS/0.5% Brij 35 prior to application of sample. Four controls wereincluded on each sample slide with feature concentrations correspondingto four anchor points on the full titration curve. The test samples wereassayed on the remaining 12 subarrays. Twenty μL of the treated samplewere then applied to each subarray. The basics of performingimmunoassays with RCA signal amplification has been described(Schweitzer et al., Nat. Biotechnol. (2002) 20:359-65) and we are usingSOPs derived from the protocols used in that study. Slides were scannedusing a LS200 scanner (TECAN). Scanned images were analyzed usingproprietary software. The fluorescence intensity of microarray spots wasanalyzed for each feature and sample, and the resulting mean intensityvalues were determined. Dose-response curves for selected cytokines wereexamined, ensuring that feature intensity is above background andexhibiting increasing intensity with increasing analyte concentration.

Subject Population for RCA Microarray Immunoassay:

For the RCA microarray immunoassays the serum from 86 subjects wasassayed. Of the 86 subjects, 28 were healthy and had an average age of60.8 years, 58 had Stage III/IV ovarian cancer and had an average age of57.1. Of the 58 patients with Stage III/IV ovarian cancer, 18 were newlydiagnosed and the remaining 40 were subjects with recurrent disease.

Subject Population for Blind Study (ELISA):

For the panel of ELISA, serum samples were collected from 100 subjectswith ovarian cancer and 106 healthy/disease-free or high-risk subjects(as part of the Yale New Haven Hospital Early Detection program(HIC10425). The normal group consisted of 66 healthy/disease-free(including the 28 healthy samples sent for the arrays) and 40 womenconsidered to be at high-risk. Of the 100 ovarian cancer patients, 24women were diagnosed with Stage I/II and 76 women with Stage III/IV EOC.Included in this group were the 18 newly diagnosed OVCA samples. Serumfrom the healthy/disease-free group served as baseline values or “normalrange” values for the presence of carcinoma.

Sera Collection:

10 ml of blood was collected, centrifuged at 1500 rpm for 10 min. andthe serum stored at −80° C. in the OB/GYN Tissue bank until further use.Collection, preparation and storage of the blood samples were doneutilizing guidelines set by the NCI Intergroup Specimen BankingCommittee. Consent for participation in this study was obtained byqualified personnel. Before analyses, the sera was thawed once and 8(25-50 ul) aliquots stored at −80° C. to ensure unnecessary freezing andthawing.

ELISA Assay:

The leptin, prolactin and IGF-II kits were purchased from DiagnosticSystems Laboratories, Inc. Webster, Tex. and the OPN (Osteopontin) fromAssay Designs, Inc. (Ann Arbor, Mich.). Assays were performed followingkit instructions, and the results were read on a Spectra Max M2Microplate Reader (Molecular Devices, Sunnyvale Calif.) set to a dualwavelength of 405 nM with the appropriate correction for each assay.Three classifiers: support vector machines (SVM), K-nearest neighborclassifiers (k-NN) and classification trees were used to analyze results(distinguish the healthy/disease/free from the ovarian cancer patients).

Statistical Analysis:

Analysis of variance (ANOVA) was used to test the significance of theprotein expression differences detected by RCA microarray immunoassaysbetween subjects with ovarian cancer and healthy subjects, using the GLMprocedure of SAS. Reported effect size measures the difference in meanbetween two groups, normalized by within group standard deviation, andis independent of the sample size:

Effect Size=(Mean_Group1−Mean_Group2)/Std_Group1_Group2

Effect size has a direct association with the predictive ability of aparticular variable. Table 9 shows conversions of effect sizes(column 1) to probability (column 2). The example presented in Table 9is intended to demonstrate the relationship between effect size andpredictive ability. For example, with an effect size of 0.3 observedbetween the two groups, the probability of correctly identifying thegroups is 0.56. With an effect size of 1, the probability increases to0.69.

TABLE 9 Effect size as the measure of predictive ability. Probabilitythat grouping could be correctly Effect Size assigned based on proteinexpression 0 0.5 0.1 0.52 0.2 0.54 0.3 0.56 0.4 0.58 0.5 0.6 0.6 0.620.7 0.64 0.8 0.66 0.9 0.67 1 0.69 1.2 0.73 1.4 0.76 1.6 0.79 1.8 0.82 20.84 2.5 0.89 3 0.93

To differentiate between normal/high risk and ovarian cancer patients,statistical cluster analysis was performed on 4 protein expressionmarkers obtained from patient serum. Three commonly used classificationmethods were used: support vector machine (SVM), k-nearest neighbors(kNN), and classification trees (Hastie, et al. 2001). We used 10-foldcross validation to evaluate the classification accuracy.

In addition to these three classification methods, we used a score-basedclassification method that is more biologically interpretable. Thescore-based classification system can be carried out as follows: (i) Foreach marker, find the best split point to minimize the number ofmisclassified subjects. The split point defines two intervals: one fornormal and another for cancer. A score of 0 is assigned to a subject ifits related observation falls in the normal interval; otherwise, a scoreof 1 is assigned. (ii) Overall, a subject is assigned a score as the sumof these assigned scores from m different markers. Therefore, the rangeof such score is between 0 and m. (iii) A given threshold (t) is used topredict the disease status for a given subject, e.g. a given subjectwith a total score equal or less than t is predicted to have normalstatus, whereas a subject with a score higher than t will be diagnosedto have disease.

The “split point” described above in connection to the described scorebased classification system may be identified as follows: Suppose thereare n samples classified into two groups. For each marker X, let x_1,x_2, . . . , x_n be the observed measurements. We screen (n−1) splitpoints y_1, y_2, . . . , y_(n−1), where y_k=0.5*(x_k+x_(k+1)) for k=1,2, . . . , n−1. For each split point y_k, there are a_1 and a_2 observedmeasurements less than y_k in the first and the second groups,respectively; and there are b_1 and b_2 observed measurements greaterthan y_k in the first and the second groups, respectively. If the leftand the right sides of y_k are assigned to the first and the secondgroups, respectively, then there are a_2 and b_1 misclassified samples.If the left and the right sides of y_k are assigned to the second andthe first groups, respectively, then there are a_1 and b_2 misclassifiedsamples. We choose the assignment that minimizes the number ofmisclassified samples.

Discussion

Ovarian cancer is a “relatively silent” disease with intra-abdominalinaccessibility which makes the monitoring and early detection of thedisease utilizing a non-invasive approach such as serum tumor markers anattractive idea. A simple, reliable, reproducible and rapid screeningstrategy with adequate sensitivity for early detection is needed toimprove our ability to accurately detect pre-malignant change or earlystage ovarian cancer in asymptomatic women at increased risk for thedevelopment of ovarian cancer. It has been suggested that, in order tobe acceptable, any screening strategy for early detection must achieve aminimum of 99.6% specificity, hence the need for a combined regimen oftests since it is unlikely that any single analyte test will be thatspecific. In fact, given the rarity of ovarian cancer, very low levelsof false positive classification will result in a large number of womenbeing incorrectly classified as potentially ovarian cancer patients ifbiomarker screening tests are used as the only means of classification.We assert that initial serum screening for a combination of analytes,followed by transvaginal ultrasound and mammography or thermal breastimaging, should provide a sufficiently low false positive rate tojustify subsequent laparoscopic surgery on individuals with detectablepelvic masses to validate the results of the diagnostic assay. Thisapproach is supported by results of studies in which the combination ofCA125 and transvaginal ultrasound detected a significant proportion ofpreclinical ovarian cancers (Jacobs, Mol Cell Proteomics 3(4):355-366(2004).

Our approach to identify serum biomarkers was based on a strategy ofscreening multiple serum proteins by high-throughput microarray analysisto identify biomarkers that had the potential to accurately discriminatebetween healthy/high-risk and cancer and still have the sensitivity todetect early stage I and II ovarian cancers. Based on the microarrayresults, a promising subset of biomarkers were selected for furtheranalysis by ELISA. Four of the biomarkers selected based on themicroarray data were confirmed as useful for early detection and a highlevel of sensitivity/specificity using ELISA analysis. Initialconfirmation of the utility of biomarkers does not require many analysesif high specificity and sensitivity is sought. Biomarkers can beeliminated based on analysis of 15-20 normal and patient samples oncequality control of the ELISA in the hands of the technician performingthe assay is established. Analysis of a larger number of samples isrequired for application of statistical techniques (such as SVM, k-NNand Tree), to determine sensitivity/specificity, split-points andcombinatorial strategies. After validating the contribution that eachbiomarker may contribute to a combined assay, split points for eachbiomarker are defined and the utility of combinations of two or moremarkers are explored statistically. Using split points the status ofeach biomarker is assigned as a binary result (normal versus abnormallevels). The number of biomarkers classified as abnormal is used todefine an individual as having or not having cancer; in this case,individuals with three or four biomarkers that are abnormal areclassified as having ovarian cancer while subjects with two or fewerbiomarkers that are abnormal are classified as not having cancer. Thebiomarkers as individual analytes may not have sufficient sensitivityand specificity, rather the combination of biomarkers may be requiredfor a diagnostic test.

Example 2: The Used of the Biomarkers Identified in Ovarian CancerPatients to Diagnose Breast Cancer and Colon Cancer

Certain samples were analyzed to determined whether the biomarkersidentified above (leptin, prolactin, OPN and IGF-II) were differentiallyexpressed in other types of cancer. The results, shown in Table 9,indicate that the biomarkers identified above can be used to diagnoseother types of cancer including breast cancer and colon cancer.

As shown in Table 10, samples corresponding to subjects with cancercould be distinguished from samples from healthy subjects by thedifferential expression of two or more biomarkers as compared to theirpredetermined standard. In Table 10, the expression levels indicated initalics and bold corresponds to expression levels outside of thepredetermined standard for said biomarker.

TABLE 10 Analysis of the expression of leptin, prolactin, OPN and IGF-IIin breast cancer and colon cancer. Patient Leptin Code SampleDescription (ng/ml) Prolactin (pg/ml) OPN (pg/ml) IGF-II (ng/ml) CA125Breast Cancer C51 Stage I Breast Cancer 2/8/00 @ Age 42

540 ND C57 Fibrocystic breast mass (No Breast Cancer) 3  0

770 8.3 C66 Mesothelial cysts/Breast Cancer (ductal) 2001/Tamox

460 ND C92 Stage I Breast Cancer 2/8/00 @ Age 42

 1

780 ND C29 Stage IV Breast Cancer

ND Colon Cancer C107 Stage 1V Colon Cancer/OVCA

ND C52 Stage 111 Colon Cancer/OV Cysts

592 ND

INCORPORATION BY REFERENCE

All of the publications cited herein are hereby incorporated byreference in their entirety to describe more fully the art to which theapplication pertains.

EQUIVALENTS

Those skilled in the art will recognize, or be able to ascertain usingno more than routine experimentation, many equivalents to the specificembodiments of the invention described herein. Such equivalents areintended to be encompassed by the following claims.

1-58. (canceled)
 59. A method comprising: (a) measuring the expressionlevel of each of at least two proteins in a sample from a femalesubject, wherein the at least two proteins are selected from prolactin,osteopontin (OPN), leptin, and macrophage migration inhibitory factor(MIF); (b) comparing the expression level of each of the measuredproteins to a reference sample for each of the measured proteins; and(c) determining if there is a significant difference in the expressionlevel of each of the measured proteins in the sample as compared to thereference sample.
 60. The method of claim 59, further comprisingmeasuring the expression level of each of at least three proteins in asample from the subject, wherein the at least three proteins areselected from prolactin, osteopontin (OPN), leptin, and MIF.
 61. Themethod of claim 59, further comprising measuring the expression level ofeach of at least four proteins in a sample from the subject, wherein theat least four proteins are selected from prolactin, osteopontin (OPN),leptin, and MIF.
 62. The method of claim 59, further comprisingdetermining if the significant difference in the expression level forprolactin, MIF and/or OPN is an increase in the expression level ofprolactin, MIF and/or OPN compared to the reference sample forprolactin, MIF and/or OPN, respectively.
 63. The method of claim 59,further comprising determining if the significant difference in theexpression level for leptin is a decrease in the expression level ofleptin compared to the reference sample for leptin.
 64. The method ofclaim 59, wherein the reference sample corresponds to: (a) theexpression level of prolactin, osteopontin (OPN), leptin, and/or MIF inhealthy subjects, or (b) the expression level of prolactin, osteopontin(OPN), leptin, and/or MIF in non-cancerous tissue from the same subject.65. The method of claim 59, wherein each protein is assigned a score of0 or 1, wherein a protein is assigned a score of 0 if the expressionlevel of the protein is not significantly different from the expressionlevel of the protein in a reference sample and wherein a protein isassigned a score of 1 if the expression level of the protein issignificantly different from the expression level of the protein in areference sample; wherein the subject is assigned an overall score thatcorresponds to the sum of the assigned scores from at least fourdifferent protein; and wherein a given threshold (t) is used.
 66. Themethod of claim 59, further comprising measuring an expression level ofcancer antigen 125 (CA 125) in the sample from the subject.
 67. Themethod of claim 59, further comprising measuring an expression level ofat least one of insulin-like growth factor (IGF-II), 6Ckine, angiotensinconverting enzyme (ACE), brain-derived neurotrophic factor (BDNF),E-Selectin, epidermal growth factor (EGF), eotaxin-2 (Eot-2), epidermalgrowth factor receptor 1 (ErbB 1), follistatin, hemofiltrate CCchemokine 4 (HCC4), herpes virus entry mediator (HVEM), insulin-likegrowth factor binding protein 2 (IGFBP-1), interleukin-17 (IL-17),interleukin 1 soluble receptor II (IL-1sRII), interleukin 2 solublereceptor alpha (IL-2 sRα), macrophage colony stimulating factor receptor(M-CSF R), macrophage inflammatory protein 1 alpha (MIP-1α), macrophageinflammatory protein 3 beta (MIP3β), matrix Metalloproteinase-8 (MMP-8),matrix metalloproteinase 7 (MMP-7), myeloid progenitor inhibitory factor1 (MPIF-1), pulmonary and activation-regulated chemokine (PARC),platelet-derived growth factor receptor beta (PDGF R β), protein C,tumor necrosis factor receptor 1 (TNF-RI), tumor necrosis factor alpha(TNF-α), soluble Vascular Adhesion Protein-1(sVAP-1), vascularendothelial growth factor receptor 2 (VEGF R2), VEGF receptor 3 (VEGFR3), human stratum corneum chymotryptic enzyme (HSCCE), kallikrein 4,kallikrein 5, kallikrein 6 (protease M), kallikrein 8, kallikrein 9,kallikrein 10, cancer antigen 125 (CA 125), CA15-3, CA19-9, OVX1,lysophosphatidic acid (LPA), carcinoembryonic antigen (CEA), macrophagecolony-stimulating factor (M-CSF), prostasin, CA54-61, CA72, HMFG2,interleukin-6 (IL-6), interleukin-10 (IL-10), LSA, NB70K, PLAP, TAG72,TPA, UGTF, WAP four-disulfide core domain 2 (HE4), matrixmetalloprotease 2, tetranectin, inhibin, mesothelin, MUC1, vascularendothelial growth factor (VEGF) NOTCH3, E2F transcription factor 3(E2F3), GTPase activating protein (RACGAP1), hemotological andneurological expressed 1 (HN1), apolipoprotein A1, laminin, claudin 3(CLDN3), claudin 4, tumor-associated calcium signal transducer 1(TROP-1/Ep-CAM), tumor-associated calcium signal transducer 2 (TROP-2),ladinin 1, S100A2, SERPIN2 (PAI-2), CD24, lipocalin 2, matriptase(TADG-15), stratifin, transforming growth factor-beta receptor III(TGF-β-RIII), platelet-derived growth factor receptor alpha, SEMACAP3,ras homology gene family member I (ARHI), thrombospondin 2,disabled-2/differentially expressed in ovarian carcinoma 2 (Dab2/DOC2),and haptoglobin-alpha subunit in the sample from the subject.
 68. Themethod of claim 59, wherein the sample is a body fluid, cell or tissuesample.
 69. The method of claim 68, wherein the body fluid sample isblood or serum.
 70. The method of claim 59, further comprising detectingan additional protein.